RECALLbot: Designing Agentic Memory and Reciprocal Disclosure for Human-Chatbot Relationships

Social chatbots are increasingly studied for their benefits in providing companionship and emotional support. These benefits rely on forming human-chatbot relationships that require credible social identity and reciprocal interaction. Memory plays a dual role: it strengthens social identity by enabling the chatbot to remember, and supports reciprocal interaction when memories are disclosed mutually. We present RECALLbot, an LLM-driven social chatbot that constructs agentic memories, including life-like Me Memory and co-constructed We Memory, and adaptively applies reciprocal disclosure strategies with user controls. In a two-week between-subjects study (N = 40), RECALLbot was compared with a baseline system lacking agentic memories and reciprocal disclosure strategies. Results show that RECALLbot enhanced perceptions of the chatbot’s social identity, elicited more frequent and deeper self-disclosures, and fostered greater trust.